Comparison of time to the event and nonlinear regression models in the analysis of germination data

Introduction: Numerous studies are being carried out nowadays to reveal the effects of different treatments on the germination of seeds from various crops.Various methods are used to calculate the parameters related to germination, among which the nonlinear regression is the most common Although different models have been introduced for this method, serious problems in its structure and results motivated researchers to investigate alternative approaches with higher accuracy and precision.The main purpose of the present research is to introduce the time-to-event model and compare its reliability with nonlinear regression in experiments carried out under different conditions. Materials and Methods:The results of four different experiments were used in this study, including the effect of Potassium cyanide on walnut seed germination, the effect of salinity on wheat seed germination, the effect of water potential on corn seed germination, and the effect of temperature on cotton seed germination.The nonlinear regression and time-to-event methods were fitted to the observed data based on the Gompertz model.The obtained standard errors from the two models were further assessed using the Monte Carlo method.Results: Both methods fitted well to the observed data according to the MSE and R 2 criteria.Although the germination parameters were approximately identical in both models, the standard error of parameters in nonlinear regression was significantly less than those of time to event method except for the experiments in which all tested seeds germinated within the time frame of study so that in the latter case the results were identical.The Monte-Carlo method confirmed the results of the time-to-event model and reveals the underestimation of the nonlinear regression method in estimating the standard error of parameters.Conclusions: Overall, the results of this research showed that the time-to-event model can be utilized as a suitable model in seed germination studies under different conditions and treatments.This model, not only provides precise estimates of the germination parameters but also provides the precise standard error of parameters that have important roles in making inferences for parameters.The drc package in R software enables researchers to fit the different time-to-event models.